






'''simple docstring'''







import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__lowerCAmelCase        =							collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__lowerCAmelCase        =							"https://storage.googleapis.com/cvdf-datasets/mnist/"


def       __UpperCamelCase					(      lowercase_       :  Tuple   ):
       """simple docstring"""




       a_						     =			numpy.dtype(numpy.uintaa   ).newbyteorder('>'   )
       return numpy.frombuffer(bytestream.read(4   )   ,       dtype=lowercase_   )[0]


@deprecated(lowercase_   ,       'Please use tf.data to implement this functionality.'   )
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       print('Extracting'   ,       f.name   )
       with gzip.GzipFile(fileobj=lowercase_   ) as bytestream:
              a_						     =			_readaa(lowercase_   )
              if magic != 2_051:
                     raise ValueError(
                         'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)   )
              a_						     =			_readaa(lowercase_   )
              a_						     =			_readaa(lowercase_   )
              a_						     =			_readaa(lowercase_   )
              a_						     =			bytestream.read(rows * cols * num_images   )
              a_						     =			numpy.frombuffer(lowercase_   ,       dtype=numpy.uinta   )
              a_						     =			data.reshape(lowercase_   ,       lowercase_   ,       lowercase_   ,       1   )
              return data


@deprecated(lowercase_   ,       'Please use tf.one_hot on tensors.'   )
def       __UpperCamelCase					(      lowercase_       :  int   ,       lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       a_						     =			labels_dense.shape[0]
       a_						     =			numpy.arange(lowercase_   ) * num_classes
       a_						     =			numpy.zeros((num_labels, num_classes)   )
       a_						     =			1
       return labels_one_hot


@deprecated(lowercase_   ,       'Please use tf.data to implement this functionality.'   )
def       __UpperCamelCase					(      lowercase_       :  List[str]   ,       lowercase_       :  int=False   ,       lowercase_       :  List[str]=10   ):
       """simple docstring"""




       print('Extracting'   ,       f.name   )
       with gzip.GzipFile(fileobj=lowercase_   ) as bytestream:
              a_						     =			_readaa(lowercase_   )
              if magic != 2_049:
                     raise ValueError(
                         'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)   )
              a_						     =			_readaa(lowercase_   )
              a_						     =			bytestream.read(lowercase_   )
              a_						     =			numpy.frombuffer(lowercase_   ,       dtype=numpy.uinta   )
              if one_hot:
                     return _dense_to_one_hot(lowercase_   ,       lowercase_   )
              return labels




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  @deprecated(
      UpperCamelCase__   ,	'Please use alternatives such as official/mnist/_DataSet.py'
      ' from tensorflow/models.'   ,	)
  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__=False   ,	UpperCamelCase__=False   ,	UpperCamelCase__=dtypes.floataa   ,	UpperCamelCase__=True   ,	UpperCamelCase__=None   ,	):






         """simple docstring"""


         a_     ,	a_						     =			random_seed.get_seed(UpperCamelCase__	)
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seeda if seed is None else seeda	)
         a_						     =			dtypes.as_dtype(UpperCamelCase__	).base_dtype
         if dtype not in (dtypes.uinta, dtypes.floataa):
                raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype	)
         if fake_data:
                a_						     =			10_000
                a_						     =			one_hot
         else:
                assert (
                    images.shape[0] == labels.shape[0]
                ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
                a_						     =			images.shape[0]

                # Convert shape from [num examples, rows, columns, depth]
                # to [num examples, rows*columns] (assuming depth == 1)
                if reshape:
                       assert images.shape[3] == 1
                       a_						     =			images.reshape(
                           images.shape[0]   ,	images.shape[1] * images.shape[2]	)
                if dtype == dtypes.floataa:
                       # Convert from [0, 255] -> [0.0, 1.0].
                       a_						     =			images.astype(numpy.floataa	)
                       a_						     =			numpy.multiply(UpperCamelCase__   ,	1.0 / 255.0	)
         a_						     =			images
         a_						     =			labels
         a_						     =			0
         a_						     =			0







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self._images







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self._labels







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self._num_examples







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self._epochs_completed







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=False   ,	UpperCamelCase__=True	):






         """simple docstring"""


         if fake_data:
                a_						     =			[1] * 784
                a_						     =			[1] + [0] * 9 if self.one_hot else 0
                return (
                    [fake_image for _ in range(UpperCamelCase__	)],
                    [fake_label for _ in range(UpperCamelCase__	)],
                )
         a_						     =			self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
                a_						     =			numpy.arange(self._num_examples	)
                numpy.random.shuffle(UpperCamelCase__	)
                a_						     =			self.images[perma]
                a_						     =			self.labels[perma]
         # Go to the next epoch
         if start + batch_size > self._num_examples:
                # Finished epoch
                self._epochs_completed += 1
                # Get the rest examples in this epoch
                a_						     =			self._num_examples - start
                a_						     =			self._images[start : self._num_examples]
                a_						     =			self._labels[start : self._num_examples]
                # Shuffle the data
                if shuffle:
                       a_						     =			numpy.arange(self._num_examples	)
                       numpy.random.shuffle(UpperCamelCase__	)
                       a_						     =			self.images[perm]
                       a_						     =			self.labels[perm]
                # Start next epoch
                a_						     =			0
                a_						     =			batch_size - rest_num_examples
                a_						     =			self._index_in_epoch
                a_						     =			self._images[start:end]
                a_						     =			self._labels[start:end]
                return (
                    numpy.concatenate((images_rest_part, images_new_part)   ,	axis=0	),
                    numpy.concatenate((labels_rest_part, labels_new_part)   ,	axis=0	),
                )
         else:
                self._index_in_epoch += batch_size
                a_						     =			self._index_in_epoch
                return self._images[start:end], self._labels[start:end]





@deprecated(lowercase_   ,       'Please write your own downloading logic.'   )
def       __UpperCamelCase					(      lowercase_       :  int   ,       lowercase_       :  List[Any]   ,       lowercase_       :  Any   ):
       """simple docstring"""




       if not gfile.Exists(lowercase_   ):
              gfile.MakeDirs(lowercase_   )
       a_						     =			os.path.join(lowercase_   ,       lowercase_   )
       if not gfile.Exists(lowercase_   ):
              urllib.request.urlretrieve(lowercase_   ,       lowercase_   )  # noqa: S310
              with gfile.GFile(lowercase_   ) as f:
                     a_						     =			f.size()
              print('Successfully downloaded'   ,       lowercase_   ,       lowercase_   ,       'bytes.'   )
       return filepath


@deprecated(
    lowercase_   ,       'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'   )
def       __UpperCamelCase					(      lowercase_       :  List[str]   ,       lowercase_       :  Tuple=False   ,       lowercase_       :  List[str]=False   ,       lowercase_       :  Optional[int]=dtypes.floataa   ,       lowercase_       :  List[Any]=True   ,       lowercase_       :  Tuple=5_000   ,       lowercase_       :  Optional[int]=None   ,       lowercase_       :  List[str]=DEFAULT_SOURCE_URL   ,       ):
       """simple docstring"""




       if fake_data:

              def fake():
                     return _DataSet(
                         []   ,       []   ,       fake_data=lowercase_   ,       one_hot=lowercase_   ,       dtype=lowercase_   ,       seed=lowercase_   )

              a_						     =			fake()
              a_						     =			fake()
              a_						     =			fake()
              return _Datasets(train=lowercase_   ,       validation=lowercase_   ,       test=lowercase_   )

       if not source_url:  # empty string check
              a_						     =			DEFAULT_SOURCE_URL

       a_						     =			'train-images-idx3-ubyte.gz'
       a_						     =			'train-labels-idx1-ubyte.gz'
       a_						     =			't10k-images-idx3-ubyte.gz'
       a_						     =			't10k-labels-idx1-ubyte.gz'

       a_						     =			_maybe_download(
           lowercase_   ,       lowercase_   ,       source_url + train_images_file   )
       with gfile.Open(lowercase_   ,       'rb'   ) as f:
              a_						     =			_extract_images(lowercase_   )

       a_						     =			_maybe_download(
           lowercase_   ,       lowercase_   ,       source_url + train_labels_file   )
       with gfile.Open(lowercase_   ,       'rb'   ) as f:
              a_						     =			_extract_labels(lowercase_   ,       one_hot=lowercase_   )

       a_						     =			_maybe_download(
           lowercase_   ,       lowercase_   ,       source_url + test_images_file   )
       with gfile.Open(lowercase_   ,       'rb'   ) as f:
              a_						     =			_extract_images(lowercase_   )

       a_						     =			_maybe_download(
           lowercase_   ,       lowercase_   ,       source_url + test_labels_file   )
       with gfile.Open(lowercase_   ,       'rb'   ) as f:
              a_						     =			_extract_labels(lowercase_   ,       one_hot=lowercase_   )

       if not 0 <= validation_size <= len(lowercase_   ):
              a_						     =			(
                  'Validation size should be between 0 and '
                  F'{len(lowercase_   )}. Received: {validation_size}.'
              )
              raise ValueError(lowercase_   )

       a_						     =			train_images[:validation_size]
       a_						     =			train_labels[:validation_size]
       a_						     =			train_images[validation_size:]
       a_						     =			train_labels[validation_size:]

       a_						     =			{'dtype': dtype, 'reshape': reshape, 'seed': seed}

       a_						     =			_DataSet(lowercase_   ,       lowercase_   ,       **lowercase_   )
       a_						     =			_DataSet(lowercase_   ,       lowercase_   ,       **lowercase_   )
       a_						     =			_DataSet(lowercase_   ,       lowercase_   ,       **lowercase_   )

       return _Datasets(train=lowercase_   ,       validation=lowercase_   ,       test=lowercase_   )












'''simple docstring'''







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCAmelCase        =							abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)


def       __UpperCamelCase					(      lowercase_       :  Dict   ):
       """simple docstring"""




       from transformers.testing_utils import pytest_addoption_shared

       pytest_addoption_shared(lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       from transformers.testing_utils import pytest_terminal_summary_main

       a_						     =			terminalreporter.config.getoption('--make-reports'   )
       if make_reports:
              pytest_terminal_summary_main(lowercase_   ,       id=lowercase_   )




